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Qin FF, Deng FL, Huang CT, Lin SL, Huang H, Nong JJ, Wei MJ. Interaction between the albumin-bilirubin score and nutritional risk index in the prediction of post-hepatectomy liver failure. World J Gastrointest Surg 2024; 16:2127-2134. [PMID: 39087104 PMCID: PMC11287680 DOI: 10.4240/wjgs.v16.i7.2127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/11/2024] [Accepted: 06/04/2024] [Indexed: 07/22/2024] Open
Abstract
BACKGROUND Post-hepatectomy liver failure (PHLF) is the most common postoperative complication and the leading cause of death after hepatectomy. The albumin-bilirubin (ALBI) score and nutritional risk index (NRI) have been shown to assess end-stage liver disease and predict PHLF and patient survival. We hypothesized that the ALBI score and NRI interact in the prediction of PHLF. AIM To analyze the interaction between the ALBI score and NRI in PHLF in patients with hepatocellular carcinoma. METHODS This retrospective study included 186 patients who underwent hepatectomy for hepatocellular carcinoma at the Affiliated Hospital of Youjiang Medical University for Nationalities between January 2020 and July 2023. Data on patient characteristics and laboratory indices were collected from their medical records. Univariate and multivariate logistic regression were performed to determine the interaction effect between the ALBI score and NRI in PHLF. RESULTS Of the 186 patients included in the study, PHLF occurred in 44 (23.66%). After adjusting for confounders, multivariate logistic regression identified ALBI grade 2/3 [odds ratio (OR) = 73.713, 95% confidence interval (CI): 9.175-592.199] and NRI > 97.5 (OR = 58.990, 95%CI: 7.337-474.297) as risk factors for PHLF. No multiplicative interaction was observed between the ALBI score and NRI (OR = 0.357, 95%CI: 0.022-5.889). However, the risk of PHLF in patients with ALBI grade 2/3 and NRI < 97.5 was 101 times greater than that in patients with ALBI grade 1 and NRI ≥ 97.5 (95%CI: 56.445-523.839), indicating a significant additive interaction between the ALBI score and NRI in PHLF. CONCLUSION Both the ALBI score and NRI were risk factors for PHLF, and there was an additive interaction between the ALBI score and NRI in PHLF.
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Affiliation(s)
- Feng-Fei Qin
- Department of Infectious Diseases, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
| | - Feng-Lian Deng
- Department of Infectious Diseases, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
| | - Cui-Ting Huang
- Department of Renal Diseases, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
| | - Shu-Li Lin
- School of Nursing, Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
| | - Hui Huang
- Department of Infectious Diseases, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
| | - Jie-Jin Nong
- Department of Interventional Oncology, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
| | - Mei-Juan Wei
- Department of Radiation Oncology, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
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Song H, Sun H, Yang L, Gao H, Cui Y, Yu C, Xu H, Li L. Nutritional Risk Index as a Prognostic Factor Predicts the Clinical Outcomes in Patients With Stage III Gastric Cancer. Front Oncol 2022; 12:880419. [PMID: 35646673 PMCID: PMC9136458 DOI: 10.3389/fonc.2022.880419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 04/13/2022] [Indexed: 12/13/2022] Open
Abstract
ObjectiveThis study is aimed to determine the potential prognostic significance of nutritional risk index (NRI) in patients with stage III gastric cancer.MethodsA total of 202 patients with stage III gastric cancer were enrolled in this study. NRI was an index based on ideal body weight, present body weight, and serum albumin levels. All patients were divided into two groups by receiver operating characteristic curve: low NRI group (NRI<99) and high NRI group (NRI≥99). The relationship between NRI and clinicopathologic characteristics was evaluated by Chi-square test. The clinical survival outcome was analyzed by Kaplan-Meier method and compared using log-rank test. The univariate and multivariate analyses were used to detect the potential prognostic factors. A nomogram for individualized assessment of disease-free survival (DFS) and overall survival (OS). The calibration curve was used to evaluate the performance of the nomogram for predicted and the actual probability of survival time. The decision curve analysis was performed to assess the clinical utility of the nomogram by quantifying the net benefits at different threshold probabilities.ResultsThe results indicated that NRI had prognostic significance by optimal cutoff value of 99. With regard to clinicopathologic characteristics, NRI showed significant relationship with age, weight, body mass index, total protein, albumin, albumin/globulin, prealbumin, glucose, white blood cell, neutrophils, lymphocyte, hemoglobin, red blood cell, hematocrit, total lymph nodes, and human epidermal growth factor receptor 2 (P<0.05). Through the univariate and multivariate analyses, NRI, total lymph nodes, and tumor size were identified as the independent factor to predict the DFS and OS. The nomogram was used to predict the 1-, 3-, and 5-year survival probabilities, and the calibration curve showed that the prediction line matched the reference line well for 1-, 3-, and 5-year DFS and OS. Furthermore, the decision curve analysis also showed that the nomogram model yielded the best net benefit across the range of threshold probability for 1-, 3-, 5-year DFS and OS.ConclusionsNRI is described as the potential prognostic factor for patients with stage III gastric cancer and is used to predict the survival and prognosis.
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Affiliation(s)
- Haibin Song
- Department of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China
| | - Hongkai Sun
- Department of Anesthesiology, Hulunbeier People’s Hospital, Hulunbeier, China
| | - Laishou Yang
- Department of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China
| | - Hongyu Gao
- Department of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China
| | - Yongkang Cui
- Department of General Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| | - Chengping Yu
- Department of General Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| | - Haozhi Xu
- Department of General Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| | - Linqiang Li
- Department of General Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, Harbin Medical University, Harbin, China
- *Correspondence: Linqiang Li,
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Xie H, Ruan G, Zhang Q, Ge Y, Song M, Zhang X, Liu X, Lin S, Zhang X, Li X, Zhang K, Yang M, Tang M, Cong M, Shi H. Combination of Nutritional Risk Index and Handgrip Strength on the Survival of Patients with Cancer Cachexia: A Multi- Center Cohort Study. J Inflamm Res 2022; 15:1005-1015. [PMID: 35210808 PMCID: PMC8858023 DOI: 10.2147/jir.s352250] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/04/2022] [Indexed: 01/04/2023] Open
Abstract
PURPOSE The nutritional risk index (NRI) and handgrip strength (HGS) are useful indicators of nutritional and physical status, respectively, both of them can predict the prognosis of many cancers; however, the predictive accuracy of a single indicator is unsatisfactory. Whether the combination of NRI and HGS could enhance the stratification of the prognosis of patients with cancer cachexia. PATIENTS AND METHODS The study population was randomly divided into training and validation cohorts. We combined NRI and HGS and constructed the NRI-HGS score. Restricted cubic splines were used to assess the associations between NRI, HGS, and all-cause mortality. The Kaplan-Meier method was used to calculate the survival probability of the patients. The Cox proportional hazards risk model was used to analyze survival and prognostic factors. RESULTS Low NRI and low HGS were independent predictors of poor prognosis in patients with cancer cachexia. The NRI-HGS score showed a better prognostic stratification than either NRI or HGS. The co-occurrence of low NRI and low HGS was associated with an approximately 1.8-fold increased risk of mortality. The NRI-HGS score could effectively distinguish patients with a poor prognosis at different pathological stages. Furthermore, we constructed a novel prognostic nomogram based on NRI and HGS. The concordance index and calibration plot confirmed that the nomogram had good prognostic accuracy. The area under the receiver operating characteristic curve of the nomogram reached >0.8, which was much higher than that of the traditional tumor-node-metastasis staging system. The nomogram provided better prognostic stratification for patients with cancer cachexia. CONCLUSION Low NRI and low HGS are independent prognostic indicators in cancer cachexia. The combination of NRI and HGS improve prognostic stratification for patients with cancer cachexia. Our study suggests combining nutritional and physical status in future cachexia research.
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Affiliation(s)
- Hailun Xie
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, People’s Republic of China
| | - Guotian Ruan
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, People’s Republic of China
| | - Qi Zhang
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, People’s Republic of China
| | - Yizhong Ge
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, People’s Republic of China
| | - Mengmeng Song
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, People’s Republic of China
| | - Xi Zhang
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, People’s Republic of China
| | - Xiaoyue Liu
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, People’s Republic of China
| | - Shiqi Lin
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, People’s Republic of China
| | - Xiaowei Zhang
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, People’s Republic of China
| | - Xiangrui Li
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, People’s Republic of China
| | - Kangping Zhang
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, People’s Republic of China
| | - Ming Yang
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, People’s Republic of China
| | - Meng Tang
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, People’s Republic of China
| | - Minghua Cong
- Department of Comprehensive Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
| | - Hanping Shi
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, People’s Republic of China
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Zeng S, Li L, Hu Y, Luo L, Fang Y. Machine learning approaches for the prediction of postoperative complication risk in liver resection patients. BMC Med Inform Decis Mak 2021; 21:371. [PMID: 34969378 PMCID: PMC8719378 DOI: 10.1186/s12911-021-01731-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 12/16/2021] [Indexed: 02/08/2023] Open
Abstract
Background For liver cancer patients, the occurrence of postoperative complications increases the difficulty of perioperative nursing, prolongs the hospitalization time of patients, and leads to large increases in hospitalization costs. The ability to identify influencing factors and to predict the risk of complications in patients with liver cancer after surgery could assist doctors to make better clinical decisions. Objective The aim of the study was to develop a postoperative complication risk prediction model based on machine learning algorithms, which utilizes variables obtained before or during the liver cancer surgery, to predict when complications present with clinical symptoms and the ways of reducing the risk of complications. Methods The study subjects were liver cancer patients who had undergone liver resection. There were 175 individuals, and 13 variables were recorded. 70% of the data were used for the training set, and 30% for the test set. The performance of five machine learning models, logistic regression, decision trees-C5.0, decision trees-CART, support vector machines, and random forests, for predicting postoperative complication risk in liver resection patients were compared. The significant influencing factors were selected by combining results of multiple methods, based on which the prediction model of postoperative complications risk was created. The results were analyzed to give suggestions of how to reduce the risk of complications. Results Random Forest gave the best performance from the decision curves analysis. The decision tree-C5.0 algorithm had the best performance of the five machine learning algorithms if ACC and AUC were used as evaluation indicators, producing an area under the receiver operating characteristic curve value of 0.91 (95% CI 0.77–1), with an accuracy of 92.45% (95% CI 85–100%), the sensitivity of 87.5%, and specificity of 94.59%. The duration of operation, patient’s BMI, and length of incision were significant influencing factors of postoperative complication risk in liver resection patients. Conclusions To reduce the risk of complications, it appears to be important that the patient's BMI should be above 22.96 before the operation, and the duration of the operation should be minimized.
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Affiliation(s)
- Siyu Zeng
- Business School, Sichuan University, Chengdu, China
| | - Lele Li
- School of Labor and Human Resources, Renmin University of China, Beijing, China.
| | - Yanjie Hu
- West China School of Nursing, West China Hospital, Sichuan University, Chengdu, China
| | - Li Luo
- Business School, Sichuan University, Chengdu, China
| | - Yuanchen Fang
- Business School, Sichuan University, Chengdu, China.
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Probst P, Fuchs J, Schön MR, Polychronidis G, Stravodimos C, Mehrabi A, Diener MK, Knebel P, Büchler MW, Hoffmann K. Prospective study to evaluate the prognostic value of different nutritional assessment scores in liver surgery: NURIMAS Liver (DRKS00006340). Hepatobiliary Surg Nutr 2020; 9:400-413. [PMID: 32832492 DOI: 10.21037/hbsn.2019.06.11] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Background Malnutrition is recognised as a preoperative risk factor for patients undergoing hepatic resection. It is important to identify malnourished patients and take preventive therapeutic action before surgery. However, there is no evidence regarding which existing nutritional assessment score (NAS) is best suited to predict outcomes of liver surgery. Methods All patients scheduled for elective liver resection at the surgical department of the University Hospital of Heidelberg and the Municipal Hospital of Karlsruhe were screened for eligibility. Twelve NASs were calculated before operation, and patients were categorised according to each score as being either at risk or not at risk for malnutrition. The association of malnutrition according to each score and occurrence of at least one major complication was the primary endpoint, which was achieved using a multivariate logistic regression analysis including established risk factors in liver surgery as covariates. Results The population consisted of 182 patients. The percentage of patients deemed malnourished by the NAS varied among the different scores, with the lowest being 2.20% (Mini Nutritional Assessment) and the highest 52.20% (Nutritional Risk Classification). Forty patients (22.0%) had a major complication. None of the scores were significantly associated with major complications. Conclusions None of the twelve investigated NAS defined a state of malnutrition that was independently associated with postoperative complications. Other means of measuring malnutrition in liver surgery should be investigated prospectively.
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Affiliation(s)
- Pascal Probst
- Department of General, Visceral and Transplantation Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Juri Fuchs
- Department of General, Visceral and Transplantation Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Michael R Schön
- Department of General and Visceral Surgery, Städtisches Klinikum Karlsruhe, Moltkestraße 90, 76133 Karlsruhe, Germany
| | - Georgios Polychronidis
- Department of General, Visceral and Transplantation Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Christos Stravodimos
- Department of General and Visceral Surgery, Städtisches Klinikum Karlsruhe, Moltkestraße 90, 76133 Karlsruhe, Germany
| | - Arianeb Mehrabi
- Department of General, Visceral and Transplantation Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Markus K Diener
- Department of General, Visceral and Transplantation Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Philipp Knebel
- Department of General, Visceral and Transplantation Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Markus W Büchler
- Department of General, Visceral and Transplantation Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Katrin Hoffmann
- Department of General, Visceral and Transplantation Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
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Ni J, Zhang L. Cancer Cachexia: Definition, Staging, and Emerging Treatments. Cancer Manag Res 2020; 12:5597-5605. [PMID: 32753972 PMCID: PMC7358070 DOI: 10.2147/cmar.s261585] [Citation(s) in RCA: 160] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 06/26/2020] [Indexed: 12/26/2022] Open
Abstract
Cachexia is a multifactorial disease characterized by weight loss via skeletal muscle and adipose tissue loss, an imbalance in metabolic regulation, and reduced food intake. It is caused by factors of catabolism produced by tumors in the systemic circulation as well as physiological factors such as the imbalanced inflammatory activation, proteolysis, autophagy, and lipolysis that may occur with gastric, pancreatic, esophageal, lung cancer, liver, and bowel cancer. Cancer cachexia not only negatively affects the quality of life of patients with cancer but also reduces the effectiveness of anti-cancer chemotherapy and increases its toxicity, leading to increased cancer-related mortality and expenditure of medical resources. Currently, there are no effective medical interventions to completely reverse cachexia and no approved drugs. Adequate nutritional support is the main method of cachexia treatment, while drugs that target the inhibition of catabolism, cell damage, and excessive activation of inflammation are under study. This article reviews recent advances in the diagnosis, staging, and evaluation of cancer cachexia.
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Affiliation(s)
- Jun Ni
- Department of Pulmonary and Critical Care Medicine, Peking Union Medical Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing 100730, People's Republic of China
| | - Li Zhang
- Department of Pulmonary and Critical Care Medicine, Peking Union Medical Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing 100730, People's Republic of China
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Bao X, Liu F, Lin J, Chen Q, Chen L, Chen F, Wang J, Qiu Y, Shi B, Pan L, Lin L, He B. Nutritional assessment and prognosis of oral cancer patients: a large-scale prospective study. BMC Cancer 2020; 20:146. [PMID: 32087695 PMCID: PMC7036168 DOI: 10.1186/s12885-020-6604-2] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 02/04/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND To evaluate and compare the prognostic performance of four nutritional indicators body mass index (BMI), serum albumin (ALB), prognostic nutritional index (PNI) and nutritional risk index (NRI) in oral cancer patients, and to predict the response to chemotherapy in patients with different nutritional status. METHODS This prospective study which involved 1395 oral cancer patients was conducted in Fujian, China from September 2007 to November 2018. The BMI, PNI and NRI were calculated according to the following formulas: BMI = weight / height2 (kg/m2), PNI = albumin (g/l) + 0.005 × lymphocyte (count/μl) and NRI = (1.519 × albumin, g/l) + (41.7× present/ideal body weight), respectively. The univariate and multivariate Cox proportional hazards models were used to compare the prognostic value of BMI, ALB, PNI and NRI in overall survival (OS) in oral cancer. RESULTS Patients with BMI < 18.5 kg/m2 (VS 18.5 kg/m2 ≤ BMI < 24 kg/m2) had a poor survival outcome (HR = 1.585; 95% CI: 1.207-2.082 ). ALB, PNI, NRI were inversely correlated with OS of oral cancer (HR = 0.716; 95% CI: 0.575-0.891; HR = 0.793; 95% CI: 0.633-0.992; HR = 0.588; 95% CI: 0.469-0.738, respectively). In addition, the prognostic predictive performance of NRI was superior to BMI or ALB or PNI. Interestingly, compared with patients with better nutritional status, chemotherapy was significantly associated with poorer OS in malnourished oral cancer patients. CONCLUSIONS BMI, ALB, PNI and NRI are of prognostic value in patients with oral cancer and the prognostic performance of NRI was superior to BMI or ALB or PNI. Malnutrition (BMI < 18.5 kg/m2 or ALB< 40 g/l or PNI < 49.3 or NRI < 97.5) could predict an unfavorable response to chemotherapy in oral cancer patients.
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Affiliation(s)
- Xiaodan Bao
- Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fujian, China
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Fengqiong Liu
- Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fujian, China
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Jing Lin
- Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fujian, China
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Qing Chen
- Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fujian, China
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Lin Chen
- Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fujian, China
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Fa Chen
- Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fujian, China
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Jing Wang
- Laboratory Center, The Major Subject of Environment and Health of Fujian Key Universities, School of Public Health, Fujian Medical University, Fujian, China
| | - Yu Qiu
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Fujian Medical University, Fujian, China
| | - Bin Shi
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Fujian Medical University, Fujian, China
| | - Lizhen Pan
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Fujian Medical University, Fujian, China
| | - Lisong Lin
- Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Fujian Medical University, Fujian, China.
| | - Baochang He
- Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fujian, China.
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China.
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Wang M. Comparison of prognostic value of three objective nutritional indicators in patients with hepatocellular carcinoma before radical resection. Shijie Huaren Xiaohua Zazhi 2019; 27:1263-1270. [DOI: 10.11569/wcjd.v27.i20.1263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Various nutrition-based prognostic scores, including control of nutritional status (CONUT) score, nutritional risk index (NRI), and prognostic nutritional index (PNI), are associated with survival rates in patients with various types of cancer.
AIM To compare the prognostic value of the above-mentioned scores in patients with hepatocellular carcinoma (HCC) before radical resection.
METHODS A retrospective analysis of 470 patients who underwent radical resection for HCC at the Yiwu Central Hospital from January 2007 to June 2016 was performed. Clinical pathological parameters, CONUT score, NRI, and PNI were collected and compared. The area under the receiver operating characteristic curve (AUC) was calculated to compare the predictive power of each scoring system. Univariate and multivariate analyses were performed using the COX proportional hazards model to identify risk factors associated with overall survival (OS).
RESULTS In the univariate analysis, albumin, PNI, NRI, CONUT score, and histology were significantly associated with OS in patients with HCC. PNI, NRI, and CONUT score were significantly associated with 1-year, 3-year, and 5-year HCC survival rates. NRI always had a higher AUC value than other nutrition-based prognostic scores. In the multivariate analysis, AST (hazard ratio [HR] = 1.503, P = 0.031), FIB status (HR = 1.981, P = 0.001), and NRI (HR = 1.584, P = 0.014) were independent risk factors for prognosis in patients with HCC.
CONCLUSION Our study suggests that NRI is superior to other nutrition-based prognostic scores in predicting overall survival in patients undergoing radical surgery for HCC.
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Affiliation(s)
- Miao Wang
- Department of Gastroenterology, Yiwu Central Hospital, Yiwu 322000, Zhejiang Province, China
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Nutritional risk in major abdominal surgery: NURIMAS Liver (DRKS00010923) - protocol of a prospective observational trial to evaluate the prognostic value of different nutritional scores in hepatic surgery. Int J Surg Protoc 2017; 6:5-10. [PMID: 31851731 PMCID: PMC6913555 DOI: 10.1016/j.isjp.2017.09.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Revised: 09/05/2017] [Accepted: 09/05/2017] [Indexed: 12/17/2022] Open
Abstract
Background Malnutrition is commonly known as a risk factor in surgical procedures. The nutritional status seems particularly relevant to the clinical outcome of patients undergoing hepatic resection. Thus, identifying affected individuals and taking preventive therapeutic actions before surgery is an important task. However, there are only very few studies, that investigate which existing nutritional assessment score (NAS) is suited best to predict the postoperative outcome in liver surgery. Objective Nutritional Risk in Major Abdominal Surgery (NURIMAS) Liver is a prospective observational trial that analyses the predictive value of 12 different NAS for postoperative morbidity and mortality after liver resection. Methods After admission to the surgical department of the University Hospital in Heidelberg or the municipal hospital of Karlsruhe, all patients scheduled for elective liver resection will be screened for eligibility. Participants will fill in a questionnaire and undergo a physical examination in order to evaluate nutritional status according to Nutritional Risk Index, Nutritional Risk Screening Score, Subjective Global Assessment, Malnutrition Universal Screening Tool, Mini Nutritional Assessment, Short Nutritional Assessment Questionnaire, Imperial Nutritional Screening System, Imperial Nutritional Screening System II, Nutritional Risk Classification and the ESPEN malnutrition criteria. Postoperative morbidity and mortality will be tracked prospectively throughout the postoperative course. The association of malnutrition according to each score and occurrence of at least one major complication will be analysed using both chi-squared tests and a multivariable logistic regression analysis. Already established risk factors in liver surgery will be added as covariates. Discussion NURIMAS Liver is a bicentric, prospective observational trial. The aim of this study is to investigate the predictive value of clinical nutritional assessment scores on postoperative morbidity and mortality after hepatic resection. This is necessary, as only a validated identification of malnourished patients at high risk for postoperative complications, enables targeted preventive action.
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Deng Y, Pang Q, Bi JB, Zhang X, Zhang LQ, Zhou YY, Miao RC, Chen W, Qu K, Liu C. A promising prediction model for survival in gallbladder carcinoma patients: pretreatment prognostic nutrient index. Tumour Biol 2016; 37:15773–15781. [PMID: 27722987 DOI: 10.1007/s13277-016-5396-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Accepted: 09/09/2016] [Indexed: 01/30/2023] Open
Abstract
The pretreatment nutritional and immunological status play indispensable roles in predicting the outcome of patients with various types of malignancies. The aim of the study was to investigate whether preoperative prognostic nutritional index (PNI), which simply accounts for nutritional and immunological status, was associated with overall survival (OS) in patients with gallbladder carcinoma (GBC). The retrospective study included a total of 315 GBC patients after surgery between 2002 and 2012. PNI was calculated according to the following formula: 10× serum albumin (g/dl) +0.005× total lymphocyte count (per mm3). A receiver operating characteristic (ROC) curve for survival prediction was plotted to verify the optimal cutoff value for LMR, which was set at 46.14. According the value, patients were categorized into two different groups, namely high-PNI group (n = 133) and low-PNI group (n = 182). The univariate and multivariate Cox regression models were used to identify the independent prognostic factors. The results showed that low pretreatment PNI value was significantly associated with elderly age, partial surgery procedure, and advanced tumor status such as tumor stage, node stage, and tumor-node-metastasis stage (P < 0.05). The low-PNI group had a worse OS compare with the high-PNI group (P < 0.05). Via univariate and multivariate analyses, pretreatment PNI was identified as an independent prognostic factor for OS [HR: 0.613; 95%CI: 0.448-0.838; P < 0.001]. Subgroup analyses further revealed that PNI was significantly associated with postoperative OS independent of tumor node metastasis stage and surgical procedure. In conclusion, pretreatment PNI might serve as an effective predictor to evaluate prognosis of GBC patients after surgery. Based on the findings, PNI, characterized with accessibility, objectivity and noninvasiveness, should be included in the routine assessment of GBC.
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Affiliation(s)
- Yan Deng
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Medical College, Xi'an Jiaotong University, No.277 West Yan-ta Road, Xi'an, Shaanxi Province, 710061, China
| | - Qing Pang
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Medical College, Xi'an Jiaotong University, No.277 West Yan-ta Road, Xi'an, Shaanxi Province, 710061, China
| | - Jian-Bin Bi
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Medical College, Xi'an Jiaotong University, No.277 West Yan-ta Road, Xi'an, Shaanxi Province, 710061, China
| | - Xing Zhang
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Medical College, Xi'an Jiaotong University, No.277 West Yan-ta Road, Xi'an, Shaanxi Province, 710061, China
| | - Ling-Qiang Zhang
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Medical College, Qinghai University, Xining, Qinghai Province, 810000, China
| | - Yan-Yan Zhou
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Medical College, Xi'an Jiaotong University, No.277 West Yan-ta Road, Xi'an, Shaanxi Province, 710061, China
| | - Run-Chen Miao
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Medical College, Xi'an Jiaotong University, No.277 West Yan-ta Road, Xi'an, Shaanxi Province, 710061, China
| | - Wei Chen
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Medical College, Xi'an Jiaotong University, No.277 West Yan-ta Road, Xi'an, Shaanxi Province, 710061, China
| | - Kai Qu
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Medical College, Xi'an Jiaotong University, No.277 West Yan-ta Road, Xi'an, Shaanxi Province, 710061, China
| | - Chang Liu
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Medical College, Xi'an Jiaotong University, No.277 West Yan-ta Road, Xi'an, Shaanxi Province, 710061, China.
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